Service Provider to the Rescue: How Firm Recovery of Do-It-Yourself Service Failure Turns Consumers from Competitors to Satisfied Customers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
While consumers frequently attempt to resolve their own consumption problems (i.e., do-it-yourself (DIY)), they are often unsuccessful and subsequently turn to a professional. In the present research, we consider DIY failure as a form of service failure (SF) and demonstrate that experiencing DIY service failure (DIY SF) influences consumer evaluations of subsequent firm recovery. This occurs because consumers who experience DIY SF gain greater understanding of the task (i.e., learning) through their failed attempt. This learning promotes increased appreciation of the recovering service provider’s ability, ultimately resulting in greater satisfaction with the recovery offering. We further identify mindset as a moderator of this effect, wherein those with a growth mindset are more likely to learn from failure and appreciate the abilities of the recovering service provider. By highlighting DIY SF as a novel form of SF, we demonstrate the importance of understanding customers’ prior experiences with the focal consumption problem and its solution, and of training front-line employees to better manage these customers. We test our theory across four studies using lab and field data, and close by discussing theoretical and managerial implications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it